Cross validation

This MR adds the following

• Adds cross-validation using a validation set; the best model based on the lowest validation loss is saved automatically now
• Adds trainer, extractor, training script and unit tests for FASNet architecture
• Small change in MCCNN architecture; added a flag for selecting whether to use sigmoid in eval phase.

bob/learn/pytorch/test/test.py

@@ -220,6 +220,7 @@ class DummyDataSetMCCNN(Dataset):
     sample = data, label
     return sample 
 
+
 def test_MCCNNtrainer():
 
   from ..architectures import MCCNN
@@ -229,13 +230,44 @@ def test_MCCNNtrainer():
   dataloader['train'] = torch.utils.data.DataLoader(DummyDataSetMCCNN(), batch_size=32, shuffle=True)
 
   from ..trainers import MCCNNTrainer
-  trainer = MCCNNTrainer(net, verbosity_level=3)
+  trainer = MCCNNTrainer(net, verbosity_level=3, do_crossvalidation=False)
   trainer.train(dataloader, n_epochs=1, output_dir='.')
 
   import os
   assert os.path.isfile('model_1_0.pth')
 
   os.remove('model_1_0.pth')   
+
+
+class DummyDataSetFASNet(Dataset):
+  def __init__(self):
+    pass
+  def __len__(self):
+    return 100
+  def __getitem__(self, idx):
+    data =  numpy.random.rand(3, 224,224).astype("float32")
+    label = numpy.random.randint(2)
+    sample = data, label
+    return sample 
+
+def test_FASNettrainer():
+
+  from ..architectures import FASNet
+  net = FASNet()
+
+  dataloader={}
+  dataloader['train'] = torch.utils.data.DataLoader(DummyDataSetFASNet(), batch_size=32, shuffle=True)
+
+  from ..trainers import FASNetTrainer
+  trainer = FASNetTrainer(net, verbosity_level=3,do_crossvalidation=False)
+  trainer.train(dataloader, n_epochs=1, output_dir='.')
+
+  import os
+  assert os.path.isfile('model_1_0.pth')
+
+  os.remove('model_1_0.pth') 
+
+
 class DummyDataSetGAN(Dataset):
   def __init__(self):
     pass
@@ -357,6 +389,14 @@ def test_extractors():
   data = numpy.random.rand(4, 128, 128).astype("float32")
   output = extractor(data)
   assert output.shape[0] == 1
+
+  # FASNet
+  from ..extractor.image import FASNetExtractor
+  extractor = FASNetExtractor(num_channels_used=4)
+  # this architecture expects RGB images of size 3x224x224 channel images
+  data = numpy.random.rand(3, 224, 224).astype("float32")
+  output = extractor(data)
+  assert output.shape[0] == 1
   
 def test_two_layer_mlp():
     """